{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6fbf3ef9",
   "metadata": {},
   "source": [
    "# *eht-imaging* M87 tutorial\n",
    "\n",
    "---\n",
    "\n",
    "`eht-imaging` is a Python software for radio interferometric simulation, calibration, analysis, and imaging. Code and documentation can be found on [GitHub](https://github.com/achael/eht-imaging)\n",
    "\n",
    "This notebook is an eht-imaging tutorial with an example script to produce polarized images of M87* from EHT data. This is a simplified script and should **not** be taken to be identical to the published results from eht-imaging\n",
    "\n",
    "Please refer to and cite [EHT Paper IV](https://iopscience.iop.org/article/10.3847/2041-8213/ab0e85) and [EHT Paper VII](https://iopscience.iop.org/article/10.3847/2041-8213/abe71d) for details of the actual imaging procedure used for the EHT M87 results. \n",
    "\n",
    "If you wish to use EHT 2017 data for more than this tutorial, please consult the [EHT data archive](https://eventhorizontelescope.org/for-astronomers/data) \n",
    "\n",
    "## Prerequisites\n",
    "Running this notebook requires that `eht-imaging` and its dependencies are installed (see README on [GitHub](https://github.com/achael/eht-imaging)). If you do not have `nfft` installed, you can set `ttype=direct` or `ttype=fast` in the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "01466163",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to eht-imaging! v 1.2.8 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import ehtim as eh\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.cm import ScalarMappable\n",
    "plt.rc('text', usetex=True)\n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "ttype='nfft'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d20977",
   "metadata": {},
   "source": [
    "## Load Data and Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a37591b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create directory for results\n",
    "outpath = './tutorial_results/ehtim_tutorial_m87'\n",
    "if not os.path.exists(os.path.dirname(outpath)):\n",
    "    os.makedirs(os.path.dirname(outpath))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "59497ef8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading uvfits:  ../data/hops_lo_3601_M87+zbl-dtcal_selfcal.uvfits\n",
      "no IF in uvfits header!\n",
      "POLREP_UVFITS: circ\n",
      "Number of uvfits Correlation Products: 4\n"
     ]
    }
   ],
   "source": [
    "arr = eh.array.load_txt('../arrays/EHT2017_template.txt') # array\n",
    "obs_in = eh.obsdata.load_uvfits('../data/hops_lo_3601_M87+zbl-dtcal_selfcal.uvfits')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa9e58d6",
   "metadata": {},
   "source": [
    "## Imaging Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee930f4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#############\n",
    "#Parameters\n",
    "#############\n",
    "# systematic noise fraction\n",
    "sys_noise = 0.005 \n",
    "\n",
    "# prior info\n",
    "zbl = 0.6 # zero baseline flux\n",
    "fov = 120.*eh.RADPERUAS # image fov\n",
    "npix = 64 # image size\n",
    "prior_fwhm = 60*eh.RADPERUAS #prior fwhm\n",
    "\n",
    "# imaging params\n",
    "maxit_i = 200\n",
    "selfcal_iter = 3\n",
    "data_term_i = {'vis':10, 'cphase':10, 'logcamp':10}\n",
    "reg_term_i = {'simple':100, 'tv2':1.}\n",
    "blurfrac = 1\n",
    "epsilon_tv = 1.e-10\n",
    "\n",
    "# polarimetric imaging params\n",
    "data_term_p={'m':1.,'pvis':1.}\n",
    "reg_term_p={'hw':100.,'ptv':1.}\n",
    "maxit_p=200\n",
    "polcal_iter=25 # this was 50 in the original script, but 25 looks ok\n",
    "sub_polcal_iter=3\n",
    "polcal_sites=['AZ','LM','PV','SP']\n",
    "leakage_tol=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "407ec11d",
   "metadata": {},
   "source": [
    "## Prepare data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eabb3aaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flagged 0/5877 visibilities\n"
     ]
    }
   ],
   "source": [
    "# flag JCMT and SR if present\n",
    "obs_in = obs_in.flag_sites(['JC','SR'])\n",
    "\n",
    "# replace tarr\n",
    "obs_in.tarr = arr.tarr\n",
    "\n",
    "# add scans and scan average\n",
    "obs_in.add_scans()\n",
    "obs_in = obs_in.avg_coherent(0., scan_avg=True)\n",
    "\n",
    "# Add a small amount of non-closing noise to everything \n",
    "obs_in = obs_in.add_fractional_noise(sys_noise)\n",
    "\n",
    "# Drop stokes I nans\n",
    "mask_nan = np.isnan(obs_in.data['vis']) \\\n",
    "                     + np.isnan(obs_in.data['qvis']) \\\n",
    "                     + np.isnan(obs_in.data['uvis']) \\\n",
    "                     + np.isnan(obs_in.data['vvis'])\n",
    "obs_in.data = obs_in.data[~mask_nan]\n",
    "\n",
    "# nominal resolution\n",
    "res = obs_in.res()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a2c690",
   "metadata": {},
   "source": [
    "## Stokes I imaging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b6115f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing imager data products . . .\n",
      "==============================\n",
      "Imager run 1 \n",
      "Imaging . . .\n",
      "time: 1.690179 s\n",
      "J: 30.828066\n",
      "chi2_cphase : 1.33 chi2_logcamp : 0.86 chi2_vis : 0.94 \n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT\n",
      "==============================\n",
      "==============================\n",
      "Imager run 2 \n",
      "Imaging . . .\n",
      "time: 1.902944 s\n",
      "J: 26.873559\n",
      "chi2_cphase : 1.32 chi2_logcamp : 0.82 chi2_vis : 0.88 \n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT\n",
      "==============================\n",
      "==============================\n",
      "Imager run 3 \n",
      "Imaging . . .\n",
      "time: 2.029449 s\n",
      "J: 26.712254\n",
      "chi2_cphase : 1.32 chi2_logcamp : 0.83 chi2_vis : 0.89 \n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT\n",
      "==============================\n",
      "No stations specified in self cal: defaulting to calibrating all stations!\n",
      "Computing the Model Visibilities with direct Fourier Transform...\n",
      "Producing clean visibilities from image with direct FT . . . \n",
      "Not Using Multiprocessing\n",
      "Scan 21/22 : [----------------------------  ]95%\n",
      "self_cal time: 0.502065 s\n",
      "No Calibration  Data for JC !\n",
      "No Calibration  Data for SR !\n",
      "No Calibration  Data for SP !\n"
     ]
    }
   ],
   "source": [
    "# make a prior\n",
    "gaussprior = eh.image.make_square(obs_in, npix, fov)\n",
    "gaussprior = gaussprior.add_gauss(zbl, (prior_fwhm, prior_fwhm, 0, 0, 0))\n",
    "\n",
    "# set up imager\n",
    "imgr = eh.imager.Imager(obs_in, gaussprior, gaussprior,\n",
    "                        flux=zbl, maxit=maxit_i, \n",
    "                        data_term=data_term_i, reg_term=reg_term_i,\n",
    "                        norm_reg=True,epsilon_tv=epsilon_tv)\n",
    "# iterate imaging steps\n",
    "for repeat in range(selfcal_iter):\n",
    "    imgr.make_image_I(show_updates=False)\n",
    "    init = imgr.out_last().blur_circ(blurfrac*res)\n",
    "    imgr.init_next = init\n",
    "    \n",
    "# self-calibrate    \n",
    "im_i_out = imgr.out_last()\n",
    "caltable_i_out = eh.selfcal(obs_in, im_i_out, method='both', use_grad=True, \n",
    "                            solution_interval=0., caltable=True)\n",
    "obs_i_out = caltable_i_out.applycal(obs_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b240e09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display results\n",
    "im_i_out.display(cbar_unit=['Tb'],has_title=False);\n",
    "im_i_out.blur_circ(20*eh.RADPERUAS).display(cbar_unit=['Tb'],has_title=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af6804a0",
   "metadata": {},
   "source": [
    "## Polarimetric Imaging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "633dda5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-V flagged 20/170 visibilities\n",
      "No polarimetric image in init_next!\n",
      "--initializing with 20% pol and random orientation!\n",
      "Initializing imager data products . . .\n",
      "==============================\n",
      "Imager run 1 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "No polarimetric image in init_next!\n",
      "--initializing with 20% pol and random orientation!\n",
      "Imaging . . .\n",
      "time: 3.307816 s\n",
      "J: -47.587514\n",
      "chi2_m : 4.51 chi2_pvis : 4.69 \n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT\n",
      "==============================\n",
      "==============================\n",
      "Imager run 2 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 2.156728 s\n",
      "J: -47.833291\n",
      "chi2_m : 4.37 chi2_pvis : 4.55 \n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT\n",
      "==============================\n",
      "==============================\n",
      "Imager run 3 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 1.617560 s\n",
      "J: -47.882625\n",
      "chi2_m : 4.35 chi2_pvis : 4.53 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "4.529110352391411 2.7557522346169794\n",
      "Original chi-squared: 4.5291\n",
      "New chi-squared: 2.7486\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0182+0.0390j\n",
      "   D_L: -0.0266+0.0574j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0086+0.0202j\n",
      "   D_L: -0.0079+0.0034j\n",
      "\n",
      "PV\n",
      "   D_R: -0.0171-0.0030j\n",
      "   D_L: 0.0214-0.0017j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.862890 s\n",
      "==============================\n",
      "Imager run 4 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 1.093841 s\n",
      "J: -52.964335\n",
      "chi2_m : 2.21 chi2_pvis : 2.32 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 5 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.404862 s\n",
      "J: -52.972112\n",
      "chi2_m : 2.21 chi2_pvis : 2.31 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 6 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.050741 s\n",
      "J: -52.972127\n",
      "chi2_m : 2.21 chi2_pvis : 2.31 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "5.78800962843787 1.8765269122878279\n",
      "Original chi-squared: 5.7880\n",
      "New chi-squared: 1.8609\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0273+0.0574j\n",
      "   D_L: -0.0363+0.0820j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0132+0.0265j\n",
      "   D_L: -0.0105+0.0029j\n",
      "\n",
      "PV\n",
      "   D_R: -0.0317-0.0049j\n",
      "   D_L: 0.0393-0.0025j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.359670 s\n",
      "==============================\n",
      "Imager run 7 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 1.866762 s\n",
      "J: -54.399392\n",
      "chi2_m : 1.68 chi2_pvis : 1.79 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 8 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.049516 s\n",
      "J: -54.399424\n",
      "chi2_m : 1.68 chi2_pvis : 1.79 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 9 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.049447 s\n",
      "J: -54.399451\n",
      "chi2_m : 1.68 chi2_pvis : 1.79 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "7.183783473178149 1.6223523014306687\n",
      "Original chi-squared: 7.1838\n",
      "New chi-squared: 1.6004\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0322+0.0666j\n",
      "   D_L: -0.0399+0.0932j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0155+0.0288j\n",
      "   D_L: -0.0106+0.0017j\n",
      "\n",
      "PV\n",
      "   D_R: -0.0445-0.0063j\n",
      "   D_L: 0.0544-0.0030j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.193172 s\n",
      "==============================\n",
      "Imager run 10 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.700206 s\n",
      "J: -55.012478\n",
      "chi2_m : 1.47 chi2_pvis : 1.58 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 11 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.040910 s\n",
      "J: -55.012499\n",
      "chi2_m : 1.47 chi2_pvis : 1.58 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 12 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.058279 s\n",
      "J: -55.012728\n",
      "chi2_m : 1.47 chi2_pvis : 1.58 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "8.287551411953812 1.5047028721614317\n",
      "Original chi-squared: 8.2876\n",
      "New chi-squared: 1.4784\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0345+0.0709j\n",
      "   D_L: -0.0409+0.0978j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0166+0.0295j\n",
      "   D_L: -0.0103+0.0011j\n",
      "\n",
      "PV\n",
      "   D_R: -0.0552-0.0074j\n",
      "   D_L: 0.0672-0.0033j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.561944 s\n",
      "==============================\n",
      "Imager run 13 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 2.322514 s\n",
      "J: -55.395262\n",
      "chi2_m : 1.38 chi2_pvis : 1.49 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 14 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.061889 s\n",
      "J: -55.395312\n",
      "chi2_m : 1.38 chi2_pvis : 1.49 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 15 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.074107 s\n",
      "J: -55.395391\n",
      "chi2_m : 1.38 chi2_pvis : 1.49 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "9.306617632711998 1.4452908477165434\n",
      "Original chi-squared: 9.3066\n",
      "New chi-squared: 1.4152\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0359+0.0733j\n",
      "   D_L: -0.0412+0.1004j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0170+0.0298j\n",
      "   D_L: -0.0094+0.0005j\n",
      "\n",
      "PV\n",
      "   D_R: -0.0647-0.0083j\n",
      "   D_L: 0.0785-0.0036j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.596790 s\n",
      "==============================\n",
      "Imager run 16 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.589738 s\n",
      "J: -55.647434\n",
      "chi2_m : 1.30 chi2_pvis : 1.41 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 17 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.049526 s\n",
      "J: -55.647447\n",
      "chi2_m : 1.30 chi2_pvis : 1.41 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 18 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.055360 s\n",
      "J: -55.647460\n",
      "chi2_m : 1.30 chi2_pvis : 1.41 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "10.160156762598447 1.3930382663077152\n",
      "Original chi-squared: 10.1602\n",
      "New chi-squared: 1.3598\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0366+0.0743j\n",
      "   D_L: -0.0411+0.1013j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0171+0.0299j\n",
      "   D_L: -0.0088+0.0003j\n",
      "\n",
      "PV\n",
      "   D_R: -0.0727-0.0090j\n",
      "   D_L: 0.0879-0.0037j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.416052 s\n",
      "==============================\n",
      "Imager run 19 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.390409 s\n",
      "J: -55.820334\n",
      "chi2_m : 1.25 chi2_pvis : 1.35 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 20 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.059457 s\n",
      "J: -55.820544\n",
      "chi2_m : 1.25 chi2_pvis : 1.35 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 21 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.059174 s\n",
      "J: -55.820669\n",
      "chi2_m : 1.25 chi2_pvis : 1.35 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "10.886847360517525 1.3521780923069278\n",
      "Original chi-squared: 10.8868\n",
      "New chi-squared: 1.3164\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0370+0.0748j\n",
      "   D_L: -0.0410+0.1014j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0171+0.0301j\n",
      "   D_L: -0.0084+0.0002j\n",
      "\n",
      "PV\n",
      "   D_R: -0.0794-0.0095j\n",
      "   D_L: 0.0957-0.0038j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.394114 s\n",
      "==============================\n",
      "Imager run 22 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.198323 s\n",
      "J: -55.960839\n",
      "chi2_m : 1.23 chi2_pvis : 1.33 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 23 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.102095 s\n",
      "J: -55.961160\n",
      "chi2_m : 1.23 chi2_pvis : 1.33 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 24 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.330029 s\n",
      "J: -55.961160\n",
      "chi2_m : 1.23 chi2_pvis : 1.33 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "11.650440317117068 1.3386134728441526\n",
      "Original chi-squared: 11.6504\n",
      "New chi-squared: 1.3003\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0374+0.0751j\n",
      "   D_L: -0.0408+0.1018j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0168+0.0302j\n",
      "   D_L: -0.0078+0.0001j\n",
      "\n",
      "PV\n",
      "   D_R: -0.0855-0.0102j\n",
      "   D_L: 0.1030-0.0040j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.454308 s\n",
      "==============================\n",
      "Imager run 25 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.294611 s\n",
      "J: -56.060280\n",
      "chi2_m : 1.19 chi2_pvis : 1.29 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 26 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.051535 s\n",
      "J: -56.060389\n",
      "chi2_m : 1.19 chi2_pvis : 1.29 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 27 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.042350 s\n",
      "J: -56.060442\n",
      "chi2_m : 1.19 chi2_pvis : 1.29 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "12.271681932132992 1.3135632862190234\n",
      "Original chi-squared: 12.2717\n",
      "New chi-squared: 1.2730\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0376+0.0754j\n",
      "   D_L: -0.0407+0.1018j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0168+0.0302j\n",
      "   D_L: -0.0073-0.0001j\n",
      "\n",
      "PV\n",
      "   D_R: -0.0905-0.0107j\n",
      "   D_L: 0.1088-0.0043j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.507052 s\n",
      "==============================\n",
      "Imager run 28 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.283777 s\n",
      "J: -56.126677\n",
      "chi2_m : 1.17 chi2_pvis : 1.27 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 29 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.044625 s\n",
      "J: -56.126700\n",
      "chi2_m : 1.17 chi2_pvis : 1.27 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 30 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.063725 s\n",
      "J: -56.126976\n",
      "chi2_m : 1.17 chi2_pvis : 1.27 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "12.808566218320038 1.298173768505469\n",
      "Original chi-squared: 12.8086\n",
      "New chi-squared: 1.2558\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0377+0.0754j\n",
      "   D_L: -0.0405+0.1017j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0167+0.0303j\n",
      "   D_L: -0.0069-0.0001j\n",
      "\n",
      "PV\n",
      "   D_R: -0.0946-0.0111j\n",
      "   D_L: 0.1137-0.0045j\n",
      "\n",
      "\n",
      "leakage_cal time: 5.008937 s\n",
      "==============================\n",
      "Imager run 31 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.280078 s\n",
      "J: -56.175267\n",
      "chi2_m : 1.15 chi2_pvis : 1.26 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 32 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.087508 s\n",
      "J: -56.176187\n",
      "chi2_m : 1.16 chi2_pvis : 1.26 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 33 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.231104 s\n",
      "J: -56.176529\n",
      "chi2_m : 1.16 chi2_pvis : 1.26 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "13.287398037573592 1.2903086145002065\n",
      "Original chi-squared: 13.2874\n",
      "New chi-squared: 1.2463\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0377+0.0754j\n",
      "   D_L: -0.0404+0.1015j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0166+0.0304j\n",
      "   D_L: -0.0067-0.0000j\n",
      "\n",
      "PV\n",
      "   D_R: -0.0981-0.0113j\n",
      "   D_L: 0.1179-0.0046j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.691827 s\n",
      "==============================\n",
      "Imager run 34 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 1.457273 s\n",
      "J: -56.237970\n",
      "chi2_m : 1.16 chi2_pvis : 1.26 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 35 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.055105 s\n",
      "J: -56.238251\n",
      "chi2_m : 1.16 chi2_pvis : 1.26 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 36 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.044722 s\n",
      "J: -56.238284\n",
      "chi2_m : 1.16 chi2_pvis : 1.26 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "13.820296497477703 1.294228136038817\n",
      "Original chi-squared: 13.8203\n",
      "New chi-squared: 1.2485\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0377+0.0755j\n",
      "   D_L: -0.0403+0.1018j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0164+0.0304j\n",
      "   D_L: -0.0065-0.0001j\n",
      "\n",
      "PV\n",
      "   D_R: -0.1018-0.0116j\n",
      "   D_L: 0.1221-0.0046j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.861594 s\n",
      "==============================\n",
      "Imager run 37 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.248260 s\n",
      "J: -56.274404\n",
      "chi2_m : 1.14 chi2_pvis : 1.25 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 38 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.270624 s\n",
      "J: -56.274518\n",
      "chi2_m : 1.14 chi2_pvis : 1.25 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 39 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.235001 s\n",
      "J: -56.274518\n",
      "chi2_m : 1.14 chi2_pvis : 1.25 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "14.223894877455109 1.2848946565142225\n",
      "Original chi-squared: 14.2239\n",
      "New chi-squared: 1.2378\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0376+0.0755j\n",
      "   D_L: -0.0401+0.1017j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0163+0.0304j\n",
      "   D_L: -0.0063-0.0001j\n",
      "\n",
      "PV\n",
      "   D_R: -0.1047-0.0118j\n",
      "   D_L: 0.1254-0.0046j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.678584 s\n",
      "==============================\n",
      "Imager run 40 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.185374 s\n",
      "J: -56.296121\n",
      "chi2_m : 1.13 chi2_pvis : 1.24 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 41 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.204345 s\n",
      "J: -56.296234\n",
      "chi2_m : 1.14 chi2_pvis : 1.24 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 42 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.285641 s\n",
      "J: -56.296234\n",
      "chi2_m : 1.14 chi2_pvis : 1.24 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "14.541126161985085 1.2797023683829094\n",
      "Original chi-squared: 14.5411\n",
      "New chi-squared: 1.2315\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0376+0.0757j\n",
      "   D_L: -0.0398+0.1018j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0163+0.0303j\n",
      "   D_L: -0.0062-0.0000j\n",
      "\n",
      "PV\n",
      "   D_R: -0.1069-0.0122j\n",
      "   D_L: 0.1277-0.0048j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.656468 s\n",
      "==============================\n",
      "Imager run 43 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.181802 s\n",
      "J: -56.310342\n",
      "chi2_m : 1.13 chi2_pvis : 1.23 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 44 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.193468 s\n",
      "J: -56.310542\n",
      "chi2_m : 1.13 chi2_pvis : 1.23 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================\n",
      "Imager run 45 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.237859 s\n",
      "J: -56.310542\n",
      "chi2_m : 1.13 chi2_pvis : 1.23 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "14.813783107044097 1.2757553698594433\n",
      "Original chi-squared: 14.8138\n",
      "New chi-squared: 1.2266\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0375+0.0758j\n",
      "   D_L: -0.0396+0.1018j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0162+0.0304j\n",
      "   D_L: -0.0060-0.0001j\n",
      "\n",
      "PV\n",
      "   D_R: -0.1088-0.0125j\n",
      "   D_L: 0.1297-0.0051j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.678554 s\n",
      "==============================\n",
      "Imager run 46 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.055378 s\n",
      "J: -56.318048\n",
      "chi2_m : 1.13 chi2_pvis : 1.23 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 47 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.045306 s\n",
      "J: -56.318082\n",
      "chi2_m : 1.13 chi2_pvis : 1.23 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 48 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.054600 s\n",
      "J: -56.318263\n",
      "chi2_m : 1.13 chi2_pvis : 1.23 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "14.943502219410204 1.2752467554427698\n",
      "Original chi-squared: 14.9435\n",
      "New chi-squared: 1.2257\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0375+0.0756j\n",
      "   D_L: -0.0396+0.1017j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0162+0.0304j\n",
      "   D_L: -0.0059+0.0001j\n",
      "\n",
      "PV\n",
      "   D_R: -0.1097-0.0127j\n",
      "   D_L: 0.1307-0.0053j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.769607 s\n",
      "==============================\n",
      "Imager run 49 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.162681 s\n",
      "J: -56.325105\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 50 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.044055 s\n",
      "J: -56.325149\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 51 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.043755 s\n",
      "J: -56.325149\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "15.159072178370582 1.271926714003377\n",
      "Original chi-squared: 15.1591\n",
      "New chi-squared: 1.2216\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0376+0.0758j\n",
      "   D_L: -0.0395+0.1017j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0161+0.0305j\n",
      "   D_L: -0.0057-0.0002j\n",
      "\n",
      "PV\n",
      "   D_R: -0.1112-0.0129j\n",
      "   D_L: 0.1322-0.0054j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.780174 s\n",
      "==============================\n",
      "Imager run 52 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.300855 s\n",
      "J: -56.329784\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 53 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.298994 s\n",
      "J: -56.329784\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 54 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.290934 s\n",
      "J: -56.329784\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "15.265810907576721 1.2716491963915573\n",
      "Original chi-squared: 15.2658\n",
      "New chi-squared: 1.2210\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0376+0.0758j\n",
      "   D_L: -0.0396+0.1017j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0161+0.0304j\n",
      "   D_L: -0.0058-0.0001j\n",
      "\n",
      "PV\n",
      "   D_R: -0.1119-0.0130j\n",
      "   D_L: 0.1329-0.0054j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.909251 s\n",
      "==============================\n",
      "Imager run 55 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.054733 s\n",
      "J: -56.331107\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 56 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.052019 s\n",
      "J: -56.331121\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 57 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.309644 s\n",
      "J: -56.331394\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "15.347597667021112 1.2718758556451024\n",
      "Original chi-squared: 15.3476\n",
      "New chi-squared: 1.2209\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0376+0.0756j\n",
      "   D_L: -0.0395+0.1016j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0162+0.0304j\n",
      "   D_L: -0.0057+0.0001j\n",
      "\n",
      "PV\n",
      "   D_R: -0.1126-0.0131j\n",
      "   D_L: 0.1335-0.0055j\n",
      "\n",
      "\n",
      "leakage_cal time: 11.061814 s\n",
      "==============================\n",
      "Imager run 58 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 1.195810 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 59 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.376743 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 60 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.313374 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "15.347597667021418 1.271875855645104\n",
      "Original chi-squared: 15.3476\n",
      "New chi-squared: 1.2209\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0376+0.0756j\n",
      "   D_L: -0.0395+0.1016j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0162+0.0304j\n",
      "   D_L: -0.0057+0.0001j\n",
      "\n",
      "PV\n",
      "   D_R: -0.1126-0.0131j\n",
      "   D_L: 0.1335-0.0055j\n",
      "\n",
      "\n",
      "leakage_cal time: 5.037500 s\n",
      "==============================\n",
      "Imager run 61 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.207695 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 62 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.282222 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 63 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.334141 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "15.347597667026026 1.2718758556451628\n",
      "Original chi-squared: 15.3476\n",
      "New chi-squared: 1.2209\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0376+0.0756j\n",
      "   D_L: -0.0395+0.1016j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0162+0.0304j\n",
      "   D_L: -0.0057+0.0001j\n",
      "\n",
      "PV\n",
      "   D_R: -0.1126-0.0131j\n",
      "   D_L: 0.1335-0.0055j\n",
      "\n",
      "\n",
      "leakage_cal time: 5.063002 s\n",
      "==============================\n",
      "Imager run 64 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.321247 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 65 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.427128 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 66 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.359329 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.347597667026463 1.271875855645174\n",
      "Original chi-squared: 15.3476\n",
      "New chi-squared: 1.2209\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0376+0.0756j\n",
      "   D_L: -0.0395+0.1016j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0162+0.0304j\n",
      "   D_L: -0.0057+0.0001j\n",
      "\n",
      "PV\n",
      "   D_R: -0.1126-0.0131j\n",
      "   D_L: 0.1335-0.0055j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.690251 s\n",
      "==============================\n",
      "Imager run 67 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.329739 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 68 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.245639 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 69 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.388270 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "15.34759766702678 1.271875855645181\n",
      "Original chi-squared: 15.3476\n",
      "New chi-squared: 1.2209\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0376+0.0756j\n",
      "   D_L: -0.0395+0.1016j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0162+0.0304j\n",
      "   D_L: -0.0057+0.0001j\n",
      "\n",
      "PV\n",
      "   D_R: -0.1126-0.0131j\n",
      "   D_L: 0.1335-0.0055j\n",
      "\n",
      "\n",
      "leakage_cal time: 4.787767 s\n",
      "==============================\n",
      "Imager run 70 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.258960 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 71 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.213682 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 72 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.243538 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "15.347597667032403 1.271875855645273\n",
      "Original chi-squared: 15.3476\n",
      "New chi-squared: 1.2209\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0376+0.0756j\n",
      "   D_L: -0.0395+0.1016j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0162+0.0304j\n",
      "   D_L: -0.0057+0.0001j\n",
      "\n",
      "PV\n",
      "   D_R: -0.1126-0.0131j\n",
      "   D_L: 0.1335-0.0055j\n",
      "\n",
      "\n",
      "leakage_cal time: 7.095551 s\n",
      "==============================\n",
      "Imager run 73 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "changed observation!\n",
      "Recomputing imager data products . . .\n",
      "Imaging . . .\n",
      "time: 0.654473 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 74 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.560755 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "==============================\n",
      "Imager run 75 \n",
      "Imaging Polarization: switching to Stokes!\n",
      "Imaging . . .\n",
      "time: 0.519806 s\n",
      "J: -56.332165\n",
      "chi2_m : 1.12 chi2_pvis : 1.22 \n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "==============================\n",
      "Finding leakage for sites: ['AZ', 'LM', 'PV']\n",
      "Minimizing...\n",
      "15.34759766703241 1.271875855645267\n",
      "Original chi-squared: 15.3476\n",
      "New chi-squared: 1.2209\n",
      "\n",
      "AZ\n",
      "   D_R: 0.0376+0.0756j\n",
      "   D_L: -0.0395+0.1016j\n",
      "\n",
      "LM\n",
      "   D_R: 0.0162+0.0304j\n",
      "   D_L: -0.0057+0.0001j\n",
      "\n",
      "PV\n",
      "   D_R: -0.1126-0.0131j\n",
      "   D_L: 0.1335-0.0055j\n",
      "\n",
      "\n",
      "leakage_cal time: 6.512259 s\n"
     ]
    }
   ],
   "source": [
    "# flag zero baselines\n",
    "obs_pol_nodcal = obs_i_out.flag_uvdist(0.1e9)\n",
    "\n",
    "# initial/prior images\n",
    "init_im = im_i_out.add_const_pol(0,0)\n",
    "prior_im = gaussprior.add_const_pol(0,0)\n",
    "\n",
    "# imager\n",
    "imgr = eh.imager.Imager(obs_pol_nodcal, init_im, prior_im, flux=zbl,\n",
    "                        data_term=data_term_p, reg_term=reg_term_p,\n",
    "                        maxit=maxit_p, \n",
    "                        epsilon_tv=epsilon_tv,norm_reg=True,\n",
    "                        transform='mcv',pol='P')\n",
    "\n",
    "# iterate imaging and D-term calibration\n",
    "for repeat_pol in range(polcal_iter):\n",
    "    # image\n",
    "    for repeat in range(sub_polcal_iter):\n",
    "        imgr.make_image_P(show_updates=False)\n",
    "        imgr.init_next=imgr.out_last().blur_circ(0.,blurfrac*res)\n",
    "        imgr.maxit_next=maxit_p + 50*repeat\n",
    "\n",
    "    # D-term calibration\n",
    "    obs_dcal = eh.leakage_cal(obs_pol_nodcal, imgr.out_last(),\n",
    "                              sites=polcal_sites, leakage_tol=leakage_tol,\n",
    "                              obs_apply=obs_pol_nodcal)\n",
    "    imgr.obs_next=obs_dcal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4678a7ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No stations specified in self cal: defaulting to calibrating all stations!\n",
      "Computing the Model Visibilities with direct Fourier Transform...\n",
      "Producing clean visibilities from image with direct FT . . . \n",
      "Applying dterms in obs.tarr to clean visibilities before selfcal!\n",
      "Applying Jones Matrices to data . . . \n",
      "Warning!: in add_jones_and_noise, some SEFDs are <= 0!\n",
      "Resorting to data point sigmas, which may add too much systematic noise!\n",
      "   Applying D Term mixing: dcal-->False\n",
      "Not Using Multiprocessing\n",
      "Scan 21/22 : [----------------------------  ]95%\n",
      "self_cal time: 0.573035 s\n"
     ]
    }
   ],
   "source": [
    "# output image and observation\n",
    "im_p_out = imgr.out_last()\n",
    "obs_p_out = obs_dcal.copy()\n",
    "\n",
    "# output array with determs\n",
    "arr_p_out = eh.array.Array(obs_p_out.tarr)\n",
    "\n",
    "# self-calibrate stokes I again after D-term cal\n",
    "# TODO: always confused if this is the right order\n",
    "caltable_p_out = eh.selfcal(obs_in, im_p_out, \n",
    "                            apply_dterms=True, #!\n",
    "                            method='both', use_grad=True, \n",
    "                            solution_interval=0., caltable=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "26d44c08",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: title={'center': 'Right D-terms'}, xlabel='Real', ylabel='Imaginary'>,\n",
       "       <Axes: title={'center': 'Left D-terms'}, xlabel='Real', ylabel='Imaginary'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot dterms\n",
    "# the dterm plotting function has a bug atm, this is a hack to plot only desired sites\n",
    "arr_plot_dterm = arr_p_out.copy()\n",
    "for site in arr_p_out.tarr['site']:\n",
    "    if site not in polcal_sites:\n",
    "        arr_plot_dterm = arr_plot_dterm.remove_site(site)\n",
    "arr_plot_dterm.plot_dterms(rangex=[-.2,.2],rangey=[-.2,.2],sites=polcal_sites)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7ac6293b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display output image\n",
    "im_p_out.blur_circ(20*eh.RADPERUAS).display(\n",
    "                 plotp=True,pcut=0.2,mcut=0.07,\n",
    "                 label_type='scale',scalecolor='k',\n",
    "                 cfun='gray_r',cbar_unit=['Tb'],has_cbar=False,\n",
    "                 vec_cfun='rainbow',nvec=18,vec_cbar_lims=(0,1),scale_ticks=True,\n",
    "                 beamparams=[20*eh.RADPERUAS,20*eh.RADPERUAS,0],beamcolor='k',\n",
    "                 has_title=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8701972b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save ouptut\n",
    "im_p_out.save_fits(outpath+'_output.fits')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
